Lexically Grounded Subword Segmentation (2406.13560v2)

Abstract: We present three innovations in tokenization and subword segmentation. First, we propose to use unsupervised morphological analysis with Morfessor as pre-tokenization. Second, we present an algebraic method for obtaining subword embeddings grounded in a word embedding space. Based on that, we design a novel subword segmentation algorithm that uses the embeddings, ensuring that the procedure considers lexical meaning. Third, we introduce an efficient segmentation algorithm based on a subword bigram model that can be initialized with the lexically aware segmentation method to avoid using Morfessor and large embedding tables at inference time. We evaluate the proposed approaches using two intrinsic metrics and measure their performance on two downstream tasks: part-of-speech tagging and machine translation. Our experiments show significant improvements in the morphological plausibility of the segmentation when evaluated using segmentation precision on morpheme boundaries and improved R\'enyi efficiency in 8 languages. Although the proposed tokenization methods do not have a large impact on automatic translation quality, we observe consistent performance gains in the arguably more morphological task of part-of-speech tagging.

Insights into Lexically Grounded Subword Segmentation

The paper "Lexically Grounded Subword Segmentation" introduces a series of novel approaches aimed at enhancing the tokenization process, particularly for tasks involving NLP. The authors present three main contributions addressing pre-tokenization, subword embedding, and segmentation efficiency, offering improvements especially in morphologically relevant tasks.

Statistical subword segmentation models such as BPE and Unigram are pervasive in NLP applications. However, these models often overlook the morphological structure of language, leading to potential shortcomings in linguistically sensitive applications. Recognizing this gap, the authors propose an overview of traditional statistical methods with linguistic insights to deliver more morphologically coherent subword segmentations.

Key Innovations Presented

1. Unsupervised Morphological Analysis in Pre-tokenization: The authors propose the use of Morfessor, an unsupervised tool for morphological segmentation, to improve the initial tokenization step. This contrasts with the typical word-like unit pre-tokenization, by offering a morphological grounding to the initial parsing of a text.
2. Algebraic Subword Embeddings: Building on the word embedding space, the authors develop an algebraic method for generating subword embeddings. This innovation ensures that segmentations reflect lexical meaning, critical for accurately modeling morphological boundaries within words.
3. Efficient Subword Bigram Model: The introduction of a subword bigram model offers an efficient alternative for segmentation, suitable for instance when avoiding the use of Morfessor and large embedding tables during inference.

Methodology and Experimental Design

The proposed tokenization methods are evaluated using intrinsic metrics such as segmentation precision at morpheme boundaries and Rènyi efficiency across eight languages. Additionally, the performance is validated extrinsically via two downstream tasks: Part-of-Speech (POS) tagging and machine translation. The experimental results show a marked improvement in segmentation precision, particularly when using Morfessor pre-tokenization, which significantly enhances morpheme boundary precision.

In POS tagging—a task inherently sensitive to morphological information—the proposed segmentation methods demonstrate substantial performance gains over standard BPE and Unigram tokenizations. These gains validate the hypothesis that lexically grounded segmentation can better retain linguistic structure. Surprisingly, improvements in machine translation were less pronounced, arguably due to the complexity and multifaceted nature of this task.

Implications and Future Potential

The paper's findings suggest several theoretical and practical implications. By incorporating lexical semantics into segmentations, the proposed methods significantly enhance computational morphological analysis, potentially improving the interpretability and generalization of multilingual NLP models.

The adaptable framework presented here could spur further exploration in cross-lingual transfer learning. The embedment of morphology into the segmentation process could facilitate the training of more versatile multilingual models, which is increasingly relevant given the global deployment of NLP technologies.

Despite these advancements, some limitations are evident. The reliance on the quality of available word embeddings poses a challenge for under-resourced languages. Additionally, the heuristic-based evaluation of certain linguistic mappings may introduce variance, suggesting the need for more robust, possibly language-specific evaluation frameworks.

Conclusion

"Lexically Grounded Subword Segmentation" provides a compelling exploration into enhancing morphological awareness in subword tokenization processes. By bridging statistical methods with linguistic insights, the authors offer substantive contributions to the field, paving the way for more semantically accurate and morphologically aware NLP systems. As future computational models continue to be refined, these methodologies have the potential to significantly influence both theory and application in multilingual language processing.